Hybrid model predictive control for optimizing gestational weight gain behavioral interventions
Open accessAbstract
Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.
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Model predictive control, recommender systems, agent-based modeling, or Bayesian network analysis are plausible personalization algorithm strategies.
“Model predictive control [40–43], recommender systems [44], agent-based modeling [45], or Bayesian network analysis [46] are all plausible personalization algorithm strategies.”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [40]
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Self-citation
- Authors
- Dong, Y., Rivera, D. E., Downs, D. S., Savage, J. S., Thomas, D. M., & Collins, L. M.
- Affiliations
- Arizona State University (US)
- Pennsylvania State University (US)
- Montclair State University (US)
- ORCID
- Venue
- 2013 American Control Conference
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 1970-1975
- ISSN
- 0743-1619 (ISSN-L)
- PMC
- PMC3856197
- OpenAlex
- W2139707015
- Cited by
- 23
- References
- 24
- Citations by year
- 2025: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 6 · 2017: 1 · 2016: 4 · 2014: 5 · 2013: 2
- FWCI
- 5.01
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the ... American Control Conference/Proceedings of the American Control Conference (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL119245)
- National Institute on Drug Abuse (R21DA024266, P50DA010075, K25DA021173)
- Office of Behavioral and Social Sciences Research
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- gestational weight gain, hybrid model predictive control, behavioral interventions, healthy eating habits, physical activity, self-regulation, internal model control, adaptive interventions, pregnant women
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